Passenger-first Infrastructure Asset Maintenance
Lead Participant:
MOONBILITY LTD
Abstract
The Passenger-first Infrastructure Asset Maintenance project aims to address Network Rail's **£10bn maintenance backlog** by revolutionising prioritisation strategies and increasing data visibility.
The prevailing inefficiency, marked by disjointed asset data and a lack of insights into passenger impacts during asset failures, necessitates a paradigm shift.
Our innovative solution involves the development of **an advanced asset failure prediction tool** that not only prioritises maintenance tasks but also **factors in passenger impact**. At its core is a cyber secure end-to-end data sharing process.
Predictions, encompassing both asset failures and passenger impacts, will be seamlessly visualised through a web-based, interactive, high-fidelity digital twin. This twin allows asset managers to optimise maintenance intervals and fix-on-fail planning, while providing train operators with precise information about anticipated delays. This information can then be relayed to passengers, significantly enhancing their experience.
With a specific focus on the Southern region, our modular approach ensures a gradual scaling up of the digital twin, covering assets and regions incrementally.
Beyond the efficiency gains in asset maintenance, our consortium is committed to building trust in public transport. Reliable train services will not only reduce the need for on-site inspections but also encourage a shift from self-driving vehicles to more sustainable options, thereby contributing to a substantial reduction in carbon emissions.
Key contributors to this project include _Warwick Manufacturing Group_, the _University of Cambridge,_ the _Department for Transport,_ _Network Rail_, Rail Data Marketplace by _Rail Delivery Group_, and _South Western Railway_.
The prevailing inefficiency, marked by disjointed asset data and a lack of insights into passenger impacts during asset failures, necessitates a paradigm shift.
Our innovative solution involves the development of **an advanced asset failure prediction tool** that not only prioritises maintenance tasks but also **factors in passenger impact**. At its core is a cyber secure end-to-end data sharing process.
Predictions, encompassing both asset failures and passenger impacts, will be seamlessly visualised through a web-based, interactive, high-fidelity digital twin. This twin allows asset managers to optimise maintenance intervals and fix-on-fail planning, while providing train operators with precise information about anticipated delays. This information can then be relayed to passengers, significantly enhancing their experience.
With a specific focus on the Southern region, our modular approach ensures a gradual scaling up of the digital twin, covering assets and regions incrementally.
Beyond the efficiency gains in asset maintenance, our consortium is committed to building trust in public transport. Reliable train services will not only reduce the need for on-site inspections but also encourage a shift from self-driving vehicles to more sustainable options, thereby contributing to a substantial reduction in carbon emissions.
Key contributors to this project include _Warwick Manufacturing Group_, the _University of Cambridge,_ the _Department for Transport,_ _Network Rail_, Rail Data Marketplace by _Rail Delivery Group_, and _South Western Railway_.
Lead Participant | Project Cost | Grant Offer |
|---|---|---|
| MOONBILITY LTD | £174,776 | £ 122,343 |
Participant |
||
| UNIVERSITY OF WARWICK | £74,837 | £ 74,837 |
People |
ORCID iD |
| Andre Wang (Project Manager) |